ABSTRACT: The retrospective diagnosis of a seizure, is very challenging. Many times clinicians can only use a patient?s self-reported history in this determination. Further, 20-30 % of patients reporting a seizure actually suffer from psychogenic non-epileptic seizures (PNES). These patients have the motor behavior of a seizure, but not the electrographic discharge of seizure activity. Considerable time and resources are used to monitor such PNES, patients including administering unnecessary therapy with anti-epileptic drugs. Medical resources could be more effectively used if we could confirm a seizure after the event (retrospective diagnosis). Here we propose a novel approach to retrospectively distinguish seizures from PNES after the event has occurred. We have developed an accurate blood biomarker technology to retrospectively diagnose acute brain injury. Previous studies support the premise that seizures affect blood RNA expression; accordingly. here we assess temporal RNA expression pattern changes following seizures. We present preliminary preclinical data showing the discrimination of induced epileptic seizures vs no induced seizures following hippocampal electric- stimulation in blood of animals. Here, we will validate in humans this maturing technology to assist with the discrimination of epileptic seizures from psychogenic non-epileptic seizures. To accomplish this, we will analyze RNA expression in blood samples from patients undergoing EEG monitoring in an epilepsy monitoring unit (Grady Memorial Hospital, Atlanta GA). Previous experience supports that 20% of these patients will have PNES. Once a patient has an EEG recorded event, we will collect blood at various time points and identify biomarker RNA molecules in the blood. Blood samples will be collected from patients with EEG verified and EEG non-verified seizures. These RNA expression patterns will be used to verify a predictive test for the retrospective diagnosis of seizures. A biomarker gene panel will be developed from this exploratory project for validation in a larger patient population.